The article titled “Artificial intelligence’s impact on oral healthcare in terms of clinical outcomes: a bibliometric analysis” by Faten AlQaifi, Dilaver Tengilimoglu, and Ilknur Arslan Aras provides a comprehensive bibliometric investigation into how artificial intelligence (AI) technologies have influenced clinical outcomes within the domain of oral healthcare. The study aims to evaluate the evolution and scope of AI applications in dentistry by focusing particularly on outcome-based evidence reported by clinicians, rather than merely discussing technological capabilities. This analytical focus addresses a critical gap in the existing literature, as most previous studies have concentrated on technological feasibility without detailing the clinical effectiveness of AI interventions.
To conduct the analysis, the authors systematically searched articles from 2010 to 2024 across five databases: PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar. After applying stringent inclusion and exclusion criteria, 120 English-language peer-reviewed articles focusing on clinical applications of AI in dentistry were selected. These articles were further analyzed using bibliometric software such as Rayyan, VOSviewer, and Microsoft Excel to assess citation trends, research productivity, authorship networks, institutional output, keyword usage, and AI typologies used in oral health settings.
The study highlights a significant increase in research output starting from 2018, with 2022 being the most productive year. Geographically, South Korea and Germany emerged as the most influential countries, with institutions like Wonkwang University, Yonsei University, and Charité – Universitätsmedizin Berlin contributing prominently. Notably, authors Joachim Krois and Falk Schwendicke from Germany’s Charité University were among the most cited, especially for their work utilizing convolutional neural networks (CNNs) in dental radiography.
Regarding clinical focus, the bibliometric analysis found that oral pathology, periodontology, orthodontics, and prosthodontics were the specialties most frequently explored in relation to AI. Among diseases, oral cancer, alveolar bone loss, and dental caries received the most attention. The dominant AI methodologies employed were deep learning techniques, particularly CNNs, which accounted for 80% of the total publications. These were mainly used to enhance the accuracy of diagnosis from radiographic images, often outperforming human dentists in precision.
The findings showed that AI applications in oral healthcare generally yield positive clinical outcomes. In 32% of the studies, AI demonstrated high accuracy across different specialties, while 22% reported AI systems outperforming dentists. Another 17% of the publications indicated that AI was comparable to previous models or was used as a supplementary tool. Only a small fraction—less than 6%—reported limited or negative clinical performance. Overall, approximately 95% of the clinical outcomes presented in the literature were positive, indicating AI’s transformative potential in improving diagnostic accuracy, treatment planning, and overall healthcare efficiency in dentistry.
The article concludes that AI integration in oral healthcare has rapidly evolved into a highly promising field, showing tangible improvements in patient care and clinical outcomes. However, the authors emphasize the need for further research in underexplored areas such as forensic and pediatric dentistry, as well as expanded efforts to assess AI from the perspective of patients, healthcare costs, and long-term effectiveness. The study calls for broader interdisciplinary collaboration, strategic policymaking, and enhanced training for dental professionals to fully harness AI’s capabilities in clinical practice.
Reference:
AlQaifi, F., Tengilimoglu, D., & Aras, I. A. (2024). Artificial intelligence’s impact on oral healthcare in terms of clinical outcomes: a bibliometric analysis. Journal of Health Organization and Management. Advance online publication. https://doi.org/10.1108/JHOM-06-2024-0233
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